Cloud Computing technology has been rapidly applied in different domains recently, promotes the progress of the domain’s informatization. Based on the analysis of the state of application requirement in disaster reduction and combining the characteristics of Cloud Computing technology, we present the research on the application of Cloud Computing technology in disaster reduction. First of all, we give the architecture of disaster reduction cloud, which consists of disaster reduction infrastructure as a service (IAAS), disaster reduction cloud application platform as a service (PAAS) and disaster reduction software as a service (SAAS). Secondly, we talk about the standard system of disaster reduction in five aspects. Thirdly, we indicate the security system of disaster reduction cloud. Finally, we draw a conclusion the use of cloud computing technology will help us to solve the problems for disaster reduction and promote the development of disaster reduction.
MODIS (Moderate Resolution Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer) is carrying
on a major satellite remote sensing sensors of EOS series in the United States. MODIS remote sensing data is the new
generation of satellite remote sensing information sources; it has broad application prospects in ecological research,
environmental monitoring, global climate change and agricultural resources survey and other studies. MODIS data has
featured a large volume of data and dealing with complex. In this paper Grid Computing technology brought to the
processing of MODIS L1B Data is in order to improve the efficiency. First of all, this paper gives a brief introduction of
MODIS L1B data and its application status, also talks about gird computing. Then the structure of MODIS L1B Data
Process Based on Grid Computing (MLDPGRID) on logic is given, also explain the function of three tiers. In the
realization section, receiving of MODIS L1B data, Grid Platform, software environment and network architecture,
processing of MODIS L1B data and portal of MLDPGRID are all discussed. Finally, the paper gives the evaluation and
conclusion of the MLDPGRID, meanwhile the optimization strategy and future work are discussed.